Efficient seeding for error-prone sequences with SubseqHash2.

ABSTRACT:

Motivation

Seeding is an essential preparatory step for many fundamental computational tasks that require large-scale sequence comparison. Substring-based seeding methods such as kmers are ideal for sequences with low error rates but struggle to achieve high sensitivity while maintaining a reasonable precision for error-prone long reads. SubseqHash, a novel subsequence-based seeding method we recently developed, achieves superior accuracy to substring-based methods in seeding sequences with high mutation/error rates, while the only drawback is its computation speed.

Results

We propose SubseqHash2, an improved algorithm that can compute multiple sets of seeds in one run, by defining k orders over all length-k subsequences and finding the optimal subsequence under each of the

SUBMITTER: Li X 

PROVIDER: S-EPMC12317743 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature